An Online MCQ sub-system for CrsMgr
Maria G. Ratcheva, Reethu Navale, Bipin C. Desai · 2022
The current pandemic has led to increased use of online learning and calls for innovative self-learning techniques. Since contact with educators is limited, students are required to become more self-reliant. This endeavour includes using self-assessment tools to measure the learning progress and uncover the areas for further studies. In this paper, we focus on a system that automatically generates various types of questions from the recommended course material. The system applies the most recent machine learning techniques, such as transfer learning, natural language generation methods and finding semantic similarity. We propose a human-in-the-loop approach where the instructor can provide his guidance. Our system would help students to calibrate themselves in a typical remote learning environment.